Objective To observe the efficacy and safety of subretinal injection of Aflibercept for the treatment of refractory or recurrent polypoidal choroidal vasculopathy (PCV). MethodsA prospective clinical research. From January to June 2022, 18 patients of 18 eyes with PCV diagnosed in The Affiliated Eye Hospital of Nanchang University were included in the study. All patients underwent best corrected visual acuity (BCVA), indocyanine green angiography and optical coherence tomography (OCT). The BCVA examination was performed using the international standard visual acuity chart, which was converted to logarithm of the minimum angle of resolution (logMAR) visual acuity during statistics. The large choroidal vessel thickness (LVCT), central retinal thickness (CRT), sub-foveal choroidal thickness (SFCT) and retinal pigment epithelium detachment (PED) height were measured by enhanced depth imaging technique of OCT. The choroidal vascular index (CVI) was calculated. There were 18 patients of 18 eyes, 11 males of 11 eyes and 7 females of 7 eyes. The age was (64.22±3.86) years old. The disease duration was (5.22±1.80) years. The patient had received intravitreal injection of anti-vascular endothelial growth factor (VEGF) drugs for (7.72±1.36) times. The logMAR BCVA of the affected eyes was 1.28±0.25. The SFCT, CRT, LVCT, PED height were (436.56±9.80), (432.44±44.29), (283.78±27.10), (342.44±50.18) μm, respectively, and CVI was 0.65±0.01. All eyes were treated with a single subretinal injection of 40 mg/ml Aflibercept 0.05 ml (including Aflibercept 2.0 mg). According to the results of OCT and BCVA after treatment, the lesions were divided into active type and static type. The active lesions were treated with intravitreal injection of Aflibercept at the same dose as before. Quiescent lesions were followed up. Examinations were performed 1-3, 6, 9 and 12 months after treatment using the same equipment and methods before treatment. The BCVA, LVCT, CRT, SFCT, PED height, CVI, interretinal or subretinal fluid, lesion regression rate, injection times, and complications during and after treatment were observed. The BCVA, SFCT, CRT, LVCT, PED height and CVI before and after treatment were compared by repeated measures analysis of variance. ResultsEighteen eyes received subretinal and/or intravitreal injection of Aflibercept (1.61±0.85) times (1-4 times). At the last follow-up, the polypoid lesions regressed in 4 eyes and PED disappeared in 1 eye. Compared with before treatment, BCVA (F=50.298) gradually increased, CRT (F=25.220), PED height (F=144.16), SFCT (F=69.77), LVCT (F=136.69), CVI (F=72.70) gradually decreased after treatment. The differences were statistically significant (P＜0.001). Macular hole occurred in 1 eye after treatment, and the hole closed spontaneously 3 months after treatment. No serious complications such as retinal tear, retinal detachment, endophthalmitis and vitreous hemorrhage occurred during and after treatment. ConclusionSubretinal injection of Aflibercept is safe and effective in the treatment of refractory PCV.
Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.
目的 為避免選擇和發表偏倚,系統評價者應采用多種查詢技術,并盡力獲得未發表的研究.本文試圖探討,英特網檢索對鑒定未發表和正在進行的臨床試驗是否有用.研究設計 利用七個Cochrane系統評價的查詢策略回顧性地在英特網上檢索未納入的隨機對照試驗.方法 檢索策略 以普通檢索式"研究方法學 NEAR干預措施NERA 條件"、用AltaVista在英特網上搜索.測量指標包括搜索時間、英特網搜索已發表研究的回溯率、精確度(已發表和未發表的隨機臨床試驗鏈接的網頁比例)、英特網檢索到的未納入的未發表和正在進行的研究數.結果 用21小時查詢了429個網頁,找到14個鏈接到未發表的、正在進行的或最近完成的試驗,至少有9個與4篇系統評價相關.英特網檢索已發表研究文獻的回溯率在0～43.6%,其鏈接已發表和未發表研究的精確度在0～20.2%.結論 未發表尤其是正在進行的試驗的信息可在英特網上找到.潛在的問題是如何評價未經同行評審的電子出版物的質量.急需更強的搜索工具.建議用"Open Trial Initiative"定義英特網發表試驗的語法,以加強試驗登記的共同操作性.因此,專門的搜索引擎可找到更多有關正在進行和已完成的臨床試驗信息.
Cardiovascular disease and cancer are the two leading chronic conditions contributing to global mortality. With the rising incidence of cancer, the prevalence of cancer therapy-related cardiovascular complications has also increased, driving the development of the emerging field of cardio-oncology. The advancement of precision medicine offers new opportunities for the individualized and targeted management of cardiovascular toxicities associated with cancer treatment. Artificial intelligence (AI) has the potential to overcome traditional limitations in medical data integration, dynamic monitoring, and interdisciplinary collaboration, thereby accelerating the application of precision medicine in cardio-oncology. By enabling personalized treatment and reducing cardiovascular complications in cancer patients, AI serves as a critical tool in this domain. This article provides an in-depth interpretation of the “Artificial intelligence to enhance precision medicine in cardio-oncology: a scientific statement from the American Heart Association” aiming to inform the integration of AI into precision medicine in China. The goal is to promote its application in the management of cardiovascular diseases related to cancer therapy and to achieve precision management in this context.
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutations, targeted drugs can be used for targeted therapy. There are many methods for detecting EGFR gene mutations, but each method has its own advantages and disadvantages. This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin (HE) staining and the patient's EGFR mutant gene. The experimental results show that the area under the curve (AUC) of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4% on the test set, and the accuracy rate was 70.8%, which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer. In this paper, the molecular phenotypes were analyzed from the scale of the whole slides pathological images, and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model, revealing the correlation between the whole slides pathological images and EGFR gene mutation risk. It could provide a promising research direction for this field.
Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disease caused by the mutations in the NF1 gene, with an incidence of approximately 1/3 000. Affecting multiple organs and systems throughout the body, NF1 caused a wide variety of clinical symptoms. A comprehensive multidisciplinary diagnostic and treatment model is needed to meet the diverse needs of NF1 patients and improve their quality of life. In recent years, the emergence of targeted therapies has further benefited NF1 patients, and the number of clinical consultations has increased dramatically. However, due to the rarity of the disease itself and insufficient attention previously, the standardized, systematic, and precise diagnosis and treatment model of NF1 still needs to be further improved. In this paper, we reviewed the current status of comprehensive diagnosis and treatment of NF1 in China, combine with our long-term experiences in diagnosis and treatment of this disease. Meanwhile, we propose future directions and several suggestions for the comprehensive diagnosis and treatment model for Chinese NF1 patients.
Currently, in precision cardiac surgery, there are still some pressing issues that need to be addressed. For example, cardiopulmonary bypass remains a critical factor in precise surgical treatment, and many core aspects still rely on the experience and subjective judgment of cardiopulmonary bypass specialists and surgeons, lacking precise data feedback. With the increasing elderly population and rising surgical complexity, precise feedback during cardiopulmonary bypass becomes crucial for improving surgical success rates and facilitating high-complexity procedures. Overcoming these key challenges requires not only a solid medical background but also close collaboration among multiple interdisciplinary fields. Establishing a multidisciplinary team encompassing professionals from the medical, information, software, and related industries can provide high-quality solutions to these challenges. This article shows several patents from a collaborative medical and electronic information team, illustrating how to identify unresolved technical issues and find corresponding solutions in the field of precision cardiac surgery while sharing experiences in applying for invention patents.
ObjectiveTo summarize the application of radiomics in colorectal cancer.MethodsRelevant literatures about the therapeutic decision-making, therapeutic, and prognostic evaluation of colorectal cancer using radiomics were collected to make an review.ResultsRadiomics is of great value in preoperative stages, therapeutic, and prognostic evaluation in colorectal cancer.ConclusionRadiomics is an important part of precision medical imaging for colorectal cancer.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
Lung cancer is one of the leading causes of cancer deaths worldwide. Many options including surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy have been applied in the treatment for lung cancer patients. However, how to develop individualized treatment plans for patients and accurately determine the prognosis of patients is still a very difficult clinical problem. In recent years, radiomics, as an emerging method for medical image analysis, has gradually received the attention from researchers. It is based on the assumption that medical images contain a vast amount of biological information about patients that is difficult to identify with naked eyes but can be accessed by computer. One of the most common uses of radiomics is the diagnosis and treatment of non-small cell lung cancer (NSCLC). In this review, we reviewed the current researches on chest CT-based radiomics in the diagnosis and treatment of NSCLC and provided a brief summary of the current state of research in this field, covering various aspects of qualitative diagnosis, efficacy prediction, and prognostic analysis of lung cancer. We also briefly described the main current technical limitations of this technology with the aim of gaining a broader understanding of its potential role in the diagnosis and treatment of NSCLC and advancing its development as a tool for individualized management of NSCLC patients.